The Meertens Tune Collections

With the Meertens Tune Collections (MTC) the Meertens Instituut provides a rich set of collections of musical data for research purposes, such as musicological investigations or music information retrieval tasks. Over the past decades, these data have been collected in the Database of Dutch Songs. The online interface of the Database of Dutch songs provides access at the level of individual records through extensive search and browse functionality. With the MTC, several collections are provided as a whole.

Contents

MTC currently consist of the following collections:

MTC-OGLAUDIO-1.0

Version:1.0
Description:Collection Onder de groene linde: 7,178 audio recordings collected by Dutch field workers during the 1950s-1980s.
Data types:mp3

MTC-OGLSCANS-1.0

Version:1.0
Description:Scans of 3,754 transcriptions of recordings from Onder de groene linde as made during the 1950s-1980s. The music is hand-written, the lyrics are typed.
Data types:jpg

MTC-FS-INST-2.0

Version:2.0
Description:18,618 digitally encoded pieces both instrumental and vocal, and both from oral culture and from written sources.
Data types:**kern, midi, lilypond, pdf, txt

MTC-FS-1.0

Version:1.0
Description:4,120 digitally encoded vocal folk songs both from Onder de groene linde (2,503) and from various related written sources (1,617).
Data types:**kern, midi, lilypond, png, pdf, txt

MTC-INST-1.0

Version:1.0
Description:2,368 digitally encoded instrumental tunes from 18th-century Dutch manuscripts and printed scores.
Data types:**kern, midi, lilypond, png, pdf

MTC-ANN-1.0

Version:1.0
Description:Annotated Corpus: 360 melodies used in various publications.
Data types:**kern, midi, png, pdf

MTC-ANN-2.0.1

Version:2.0.1
Description:Annotated Corpus: 360 melodies used in various publications, with additional meta data.
Data types:**kern, midi,lilypond, png, pdf, txt

MTC-LC-1.0

Version:1.0
Description:Large Corpus: 4,830 melodies used in various publications.
Data types:**kern, midi

MTC-FS-INST is the successor of both MTC-FS and MTC-INST. MTC-LC predates both MTC-FS and MTC-INST. It is provided because it has been used in various research publications. MTC-ANN is a subset of MTC-LC that has been carefully selected for small scale experiments and that has been annotated concerning melodic similarity and motif occurrences. It contains 26 tune families.

Documentation

A concise description of the various releases in MTC is available in the following reports:

  • Van Kranenburg, Peter & Martine de Bruin & Louis P. Grijp & Frans Wiering. “The Meertens Tune Collections”. Meertens Online Reports, No. 2014-1. Amsterdam: Meertens Instituut, 2014. download pdf.
  • Van Kranenburg, Peter & Berit Janssen & Anja Volk. “The Meertens Tune Collections: The Annotated Corpus (MTC-ANN) Versions 1.1 and 2.0.1”. Meertens Online Reports, No. 2016-1. Amsterdam: Meertens Instituut, 2016. download pdf.
  • Van Kranenburg, Peter & Martine de Bruin. “The Meertens Tune Collections: MTC-FS-INST 2.0”. Meertens Online Reports, No. 2018-1. Amsterdam: Meertens Instituut, 2018. download pdf (coming soon).

If you use this data for a research paper, please cite one of these reports.

Sampleset

To get a quick idea of the contents of MTC, a sample dataset containing a small subset of each collection is available for download: MTC-sampleset.zip

Download

To download follow this link: download form. We appreciate when you drop us your name, a comment (optionally) and your email address (optionally). The email addresses will exclusively be used for future notifications about MTC, and will under no circumstance be shared with third parties.

Posters

The Meertens Tune Collections were demonstrated during the 15th, 16th and 19th International Society for Music Information Retrieval Conferences. Download poster 2014 Download poster 2015 Download poster 2018

Long-term Availability

Permament and sustainable storage of MTC is guaranteed by the Meertens Instituut.

Contact

For questions about the Meertens Tune Collections, please contact Peter van Kranenburg: peter.van.kranenburg@meertens.knaw.nl.

Publications

The Meertens Tune Collections have been used in the following publications:

  • DeCastro-Arrazola, V. (2018). Typological tendencies in verse and their cognitive grounding. Ph.D. diss., Leiden University.
  • Kroher, N. (2018). Flamenco Music Information Retrieval: Automatic Content-Based Description of Flamenco Music Collections. Ph.D. diss., University of Seville.
  • Pesek, M. & M. Žerovnik & Aleš Leonardis & Matija Marolt (2018). Modeling song similarity with unsupervised learning. Proceedings of the 8th International Workshop on Folk Music Analysis, Thessaloniki, pp. 46-48.
  • Walshaw, C. (2018). A Visual Exploration of Melodic Relationships within Traditional Music Collections. Proc. 22nd Intl Conference on Information Visualisation, Salerno, Italy.
  • Walshaw, C. (2018). Visualising Melodic Similarities in Folk Music. Proceedings of the 8th International Workshop on Folk Music Analysis, Thessaloniki, pp. 84-85.
  • Ren, I.Y. & H.V. Koops & D. Bountouridis & A. Volk & W. Swierstra & R. Veltkamp (2018). Feature Analysis of Repeated Patterns in Dutch Folk Songs using Principal Component Analysis. Proceedings of the 8th International Workshop on Folk Music Analysis, pp. 86-87.
  • Kroher, N. & J.-M. Díaz-Báñez (2018). Audio-Based Melody Categorization: Exploring Signal Representations and Evaluation Strategies. Computer Music Journal 41(4) pp. 64-82.
  • Bountouridis, D. & D.G. Brown & F. Wiering & R.C. Veltkamp (2017). Melodic Similarity and Applications Using Biologically-Inspired Techniques. Applied Sciences 7(12), 1242, pp. 382-410.
  • Janssen, B. & P. van Kranenburg & A. Volk (2017). Finding Occurrences of Melodic Segments in Folk Songs Employing Symbolic Similarity Measures. Journal of New Music Research 46(2), pp. 118-134.
  • Panteli, M & S. Dixon. (2016). On the evaluation of rhythmic and melodic descriptors for music similarity. Proceedings of the 17th International Society for Music Information Retrieval Conference, New York. pp. 468-474.
  • Boot, P. & A. Volk & W.B. de Haas. (2016). Evaluating the Role of Repeated Patterns in Folk Song Classification and Compression. Journal of New Music Research 45 (3), pp. 223-238.
  • Bountouridis, D. & H.V. Koops & F. Wiering & R.C. Veltkamp. (2016). Music Outlier Detection Using Multiple Sequence Alignment and Independent Ensembles. Similarity Search and Applications - 9th International Conference, SISAP 2016, Tokyo, Japan, October 24-26, 2016, Proceedings. pp. 286-300.
  • Goienetxea, I. & K. Neubarth & D. Conklin. (2016). Melody classification with pattern covering. MML 2016: 9th International Workshop on Machine Learning and Music. pp. 26-30.
  • Van Kranenburg, P. & D. Conklin. (2016). A Pattern Mining Approach to Study a Collection of Dutch Folk-Songs. Proceedings of the Sixth International Workshop on Folk Music Analysis, Dublin. pp 71-73.
  • Van Balen, J. (2016). Audio Description and Corpus Analysis of Popular Music. Ph.D. diss., Utrecht University.
  • Rodríguez-López, M. (2016). Automatic Melody Segmentation. Ph.D. diss., Utrecht University.
  • Olthof, M. & B. Janssen & H. Honing. (2015). The Role Of Absolute Pitch Memory In The Oral Transmission of Folksongs. Empirical Music Review 10 (3), pp. 161-174.
  • Rodríguez-López, M.E. & A. Volk. (2015). Selective Acquisition Techniques for Enculturation-Based Melodic Phrase Segmentation. Proceedings of the 16th International Society for Music Information Retrieval Conference, Malaga. pp. 218-224.
  • Boot, P. (2015). Using Discovered and Annotated Patterns as Compression Method for determining Similarity between Folk Songs. Master Thesis, Utrecht University.
  • Janssen, B. & P. van Kranenburg. (2015). A Comparison of Symbolic Similarity Measures for Finding Occurrences of Melodic Segments. Proceedings of the 16th International Society for Music Information Retrieval Conference, Malaga. pp. 659-672.
  • Van Kranenburg, P. & F. Karsdorp. (2014). Cadence Detection in Western Traditional Stanzaic Songs using Melodic and Textual Features. Proceedings of the 15th International Society for Music Information Retrieval Conference, Taipei. pp. 391-396.
  • Van Kranenburg, P. & A. Volk & F. Wiering. (2013). A Comparison between Global and Local Features for Computational Classification of Folk Song Melodies. Journal of New Music Research 42 (1), pp. 1-18.
  • Conklin, D. (2013). Fusion functions for multiple viewpoints. MML 2013: International Workshop on Machine Learning and Music, Prague.
  • Velarde, G., T. Weyde & D. Meredith. (2013). An approach to melodic segmentation and classification based on filtering with the Haar wavelet. Journal of New Music Research 42(4), pp. 325-345.
  • Hillewaere, R., B. Manderick, and D. Conklin. (2014). Alignment methods for folk tune classification. Spiliopoulou, M. et al., (eds). Data Analysis, Machine Learning and Knowledge Discovery, Studies in Classification, Data Analysis, and Knowledge Organization. pp. 369-378.
  • Van Kranenburg, P. & A. Volk & F. Wiering. (2012). On Identifying Folk Song Melodies Employing Recurring Motifs. Proceedings of the 12th International Conference on Music Perception and Cognition and the 8th Triennial Conference of the European Society for the Cognitive Sciences of Music, Thessaloniki. pp. 1057-1062.
  • Volk, A. & P. van Kranenburg. (2012). Melodic similarity among folk songs: An annotation study on similarity-based categorization in music. Musicae Scientiae 16, issue 3, pp. 317-339.
  • Van Kranenburg, P. & A. Volk & F. Wiering. (2011). On Operationalizing the Musicological Concept of Tune Family for Computational Modeling. Maegaard, B (ed.). Proceedings of Supporting Digital Humanities: Answering the unaskable. Kopenhagen.
  • Van Kranenburg, P. (2010). A Computational Approach to Content-Based Retrieval of Folk Song Melodies. Ph.D. diss., Utrecht University.
  • Van Kranenburg, P. & G. Tzanetakis. (2010). A Computational Approach to the Modeling and Employment of Cognitive Units of Folk Song Melodies using Audio Recordings. Proceedings of the 11th International Conference on Music Perception and Cognition, Seattle.